samipsinghal/trees

Decision Tree Implementation

This repository contains the implementation of the Decision Tree algorithm as part of a course project.

Overview

This project involves the implementation of the Decision Tree algorithm, a widely-used method for classification and regression tasks. The notebook includes steps to preprocess the data, implement the Decision Tree model, and evaluate its performance. The results demonstrate the effectiveness of Decision Trees in handling classification tasks with a focus on accuracy and performance metrics.

Table of Contents

• Installation
• Usage
• Project Structure
• Examples
• Contributing
• License

Installation

To run the notebook and reproduce the results, you need to have Python and the necessary libraries installed. Follow the steps below to set up your environment:

1. Clone the repository:

   git clone https://github.com/samipsinghal/trees.git
   cd trees

2. Install the required packages:

   pip install -r requirements.txt

Usage

To use this repository, open the Decision Tree.ipynb notebook in Jupyter Notebook or Jupyter Lab. You can run the cells in the notebook to understand the steps involved in the implementation of the Decision Tree algorithm and see the results of various experiments.

Project Structure

The repository is structured as follows:

• Decision Tree.ipynb: Main Jupyter Notebook containing the project implementation.
• requirements.txt: List of Python libraries required to run the notebook.

Examples

Here are some of the key sections covered in the main notebook (Decision Tree.ipynb):

1. Introduction to Decision Trees: Overview of the Decision Tree algorithm, its importance, and applications.
2. Data Preparation: Steps to preprocess the data for training the Decision Tree model.
3. Algorithm Implementation: Detailed implementation of the Decision Tree algorithm.
4. Training and Evaluation: Training the Decision Tree model on the dataset and evaluating its performance.
5. Results and Analysis: Visualization and analysis of the results obtained from the experiments. The results demonstrate the effectiveness of Decision Trees in handling classification tasks with a focus on accuracy and performance metrics.

Contributing

Contributions are welcome! If you have any suggestions or improvements, feel free to create a pull request or open an issue.

License

This project is licensed under the MIT License. See the LICENSE file for more details.